AI Engineering Lead
On-siteHyderabad, Telangana, India
Job Summary
Lead end-to-end AI project delivery with clear governance frameworks, ensuring transparent communication of risks and technical decisions to clients and stakeholders. Design and architect robust AI systems, including RAG, agentic frameworks, and LLM-powered solutions optimized for production environments. Conduct feasibility assessments to determine optimal technical approaches, develop advanced prompt engineering techniques, and design comprehensive evaluation frameworks incorporating LLM-as-a-judge methodologies. Execute rigorous, data-driven experiments across prompts and models, documenting findings to mitigate failure modes like hallucinations and retrieval gaps. Build scalable inference infrastructure, CI/CD pipelines, and MLOps/LLMOps automation across the full lifecycle, while mentoring junior engineers and contributing to business development initiatives. Define ethical boundaries for AI systems and ensure responsible deployment.
Required Qualifications
- 6+ years of hands-on experience building, deploying, and maintaining AI solutions in production environments
- Expert-level proficiency in Python with strong software engineering practices (Git, code review, testing)
- Proven expertise in designing and implementing RAG systems, including chunking strategies, embedding models, retrieval optimization, reranking, and evaluation methodologies
- Solid experience with cloud platforms (AWS, Azure, or GCP) including containerization, orchestration, and infrastructure management
- Demonstrated track record with MLOps/LLMOps tools and frameworks (MLflow, Weights & Biases, or equivalent)
- Strong hands-on experience with LLM versioning, model management, and experiment tracking
- Practical expertise in designing evaluation frameworks, custom metrics, dataset curation, and structured experimentation
- Experience designing and implementing event-driven architectures, RESTful APIs, and microservices
- Proven ability to lead technical teams, mentor engineers, and drive collaborative problem-solving
- Excellent communication skills—equally comfortable engaging engineering teams, technical stakeholders, and senior leadership
- Strong analytical and decision-making abilities with a detail-oriented approach to complex technical challenges
- Experience defining and communicating AI system limitations, risks, and ethical considerations to diverse audiences
- Advanced English proficiency (required for effective communication with global teams and stakeholders)
Desired Qualifications
- Experience with Databricks MLOps platform or similar enterprise ML platforms
- Hands-on experience with LLM fine-tuning and transfer learning techniques
- Proven expertise building agentic GenAI systems and multi-step reasoning frameworks
- Knowledge of Infrastructure as Code (Terraform, CloudFormation, or equivalent)
- Experience implementing security, compliance, and observability solutions for AI services
- Strong background in classical machine learning and statistical methods
- Active contributions to open-source AI/ML projects
- Experience with advanced prompt engineering frameworks and tool-use optimization
- Background in hiring, team building, and organizational development
- Understanding of AI ethics, bias mitigation, and responsible AI practices
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